机器学习与定量金融简介

English | 2021 | ISBN: 1786349361 | 263 Pages | PDF (True) | 35 MB

In today's world, we are increasingly exposed to the words 'machine learning', a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. In the past few years, machine learning has been introduced to the world of finance, reshaping the landscape of quantitative finance as we know it.Introduction to Machine Learning and Quantitative Finance aims to demystify machine learning by uncovering its underlying mathematics and showing how to apply machine learning algorithms to real-world financial data problems. Each chapter introduces problems around supervised learning algorithms, including linear models, tree-based models and neural networks, as well as unsupervised learning and reinforcement learning, followed by essential definitions and theorems in each case. Detailed guidance on the practical implementation of the algorithms is provided, and all codes are available on a GitHub repository. There are also exercises at the end of each chapter for readers to self-check their understanding.This interdisciplinary textbook provides a general framework of machine learning and provides a systematic treatment of modern machine learning methods, with ample examples to enhance the reader's understanding. Introduction to Machine Learning and Quantitative Finance provides not only theoretical knowledge of machine learning but also practical examples of financial applications. It will give readers hands-on experience in the field and enable them to apply the knowledge in this book to their own financial data problems.

在当今世界，我们越来越频繁地接触到“机器学习”这个词，这似乎是一种能够治愈诸如图像识别和机器语言翻译等所有问题的灵丹妙药。近年来，机器学习被引入金融领域，并重塑了我们熟知的量化金融市场格局。《机器学习与定量金融导论》旨在通过揭示其背后的数学原理并展示如何将机器学习算法应用于实际的金融数据问题来消除对机器学习的误解。每一章都介绍了监督学习算法围绕的问题，包括线性模型、树基模型和神经网络，以及无监督学习和强化学习，并在每种情况下提供基本定义和定理。每个算法的实际实现提供了详细指导，所有的代码都可以在GitHub存储库中找到。此外，每一章节后面还设有练习题供读者自我检查理解程度。这本跨学科教材提供了一个机器学习的通用框架，并对现代机器学习方法进行了系统的处理，配有大量示例以增强读者的理解。《机器学习与定量金融导论》不仅提供了关于机器学习的理论知识，还提供了实际的金融应用示例。它将给读者带来在该领域的实践经验，并使他们能够将书中的知识应用于自己的金融数据问题上。

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